Fuzzy Learning Vector Quantization Particle Swarm Optimization (FLVQ-PSO) and Fuzzy Neuro Generalized Learning Vector Quantization (FN-GLVQ) for automatic early detection system of heart diseases based on real-time electrocardiogram

M. Febrian Rachmadi, M. Anwar Ma'sum, I. Made Agus Setiawan, Wisnu Jatmiko

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

Automatic heart beats classification has attracted much interest for research recently and we are interested to determine the type of arrhythmia from electrocardiogram (ECG) signal automatically. This paper will discuss thoroughly about study and implementation of FLVQ-PSO, an extension from FLVQ algorithm which use MSA and PSO method, and FN-GLVQ, an extension from GLVQ algorithm which use fuzzy logic concept, to classify ECG signals. By using 10-Fold Cross Validation, the algorithm produced an average accuracy 84.02%, 98.25%, 99.00%, and 97.70%, respectively for FLVQ, FLVQ-PSO, GLVQ, and FN-GLVQ.

Original languageEnglish
Title of host publication2012 Proceedings of SICE Annual Conference, SICE 2012
PublisherSociety of Instrument and Control Engineers (SICE)
Pages465-470
Number of pages6
ISBN (Print)9781467322591
Publication statusPublished - 1 Jan 2012
Event2012 51st Annual Conference on of the Society of Instrument and Control Engineers of Japan, SICE 2012 - Akita, Japan
Duration: 20 Aug 201223 Aug 2012

Publication series

NameProceedings of the SICE Annual Conference

Conference

Conference2012 51st Annual Conference on of the Society of Instrument and Control Engineers of Japan, SICE 2012
Country/TerritoryJapan
CityAkita
Period20/08/1223/08/12

Keywords

  • Arrhythmia Classification
  • Biomedical Signal Processing
  • FLVQ
  • FLVQ-PSO
  • FN-GLVQ
  • GLVQ

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